Overview

Dataset statistics

Number of variables11
Number of observations19382
Missing cells2086
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 MiB
Average record size in memory380.7 B

Variable types

Categorical3
Numeric7
Text1

Alerts

claim_status is highly overall correlated with video_download_count and 3 other fieldsHigh correlation
video_comment_count is highly overall correlated with video_download_count and 3 other fieldsHigh correlation
video_download_count is highly overall correlated with claim_status and 4 other fieldsHigh correlation
video_like_count is highly overall correlated with claim_status and 4 other fieldsHigh correlation
video_share_count is highly overall correlated with claim_status and 4 other fieldsHigh correlation
video_view_count is highly overall correlated with claim_status and 4 other fieldsHigh correlation
verified_status is highly imbalanced (65.7%)Imbalance
claim_status has 298 (1.5%) missing valuesMissing
video_transcription_text has 298 (1.5%) missing valuesMissing
video_view_count has 298 (1.5%) missing valuesMissing
video_like_count has 298 (1.5%) missing valuesMissing
video_share_count has 298 (1.5%) missing valuesMissing
video_download_count has 298 (1.5%) missing valuesMissing
video_comment_count has 298 (1.5%) missing valuesMissing
video_id has unique valuesUnique
video_download_count has 977 (5.0%) zerosZeros
video_comment_count has 3434 (17.7%) zerosZeros

Reproduction

Analysis started2026-01-04 20:26:07.718472
Analysis finished2026-01-04 20:26:14.708008
Duration6.99 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

claim_status
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing298
Missing (%)1.5%
Memory size1.0 MiB
claim
9608 
opinion
9476 

Length

Max length7
Median length5
Mean length5.9930832
Min length5

Characters and Unicode

Total characters114372
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclaim
2nd rowclaim
3rd rowclaim
4th rowclaim
5th rowclaim

Common Values

ValueCountFrequency (%)
claim9608
49.6%
opinion9476
48.9%
(Missing)298
 
1.5%

Length

2026-01-04T17:26:14.816132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-04T17:26:14.884496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
claim9608
50.3%
opinion9476
49.7%

Most occurring characters

ValueCountFrequency (%)
i28560
25.0%
o18952
16.6%
n18952
16.6%
c9608
 
8.4%
a9608
 
8.4%
l9608
 
8.4%
m9608
 
8.4%
p9476
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)114372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i28560
25.0%
o18952
16.6%
n18952
16.6%
c9608
 
8.4%
a9608
 
8.4%
l9608
 
8.4%
m9608
 
8.4%
p9476
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)114372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i28560
25.0%
o18952
16.6%
n18952
16.6%
c9608
 
8.4%
a9608
 
8.4%
l9608
 
8.4%
m9608
 
8.4%
p9476
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)114372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i28560
25.0%
o18952
16.6%
n18952
16.6%
c9608
 
8.4%
a9608
 
8.4%
l9608
 
8.4%
m9608
 
8.4%
p9476
 
8.3%

video_id
Real number (ℝ)

Unique 

Distinct19382
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6274541 × 109
Minimum1.234959 × 109
Maximum9.9998731 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.6 KiB
2026-01-04T17:26:14.967540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.234959 × 109
5-th percentile1.6658012 × 109
Q13.4304168 × 109
median5.6186636 × 109
Q37.8439602 × 109
95-th percentile9.5670758 × 109
Maximum9.9998731 × 109
Range8.7649141 × 109
Interquartile range (IQR)4.4135434 × 109

Descriptive statistics

Standard deviation2.5364405 × 109
Coefficient of variation (CV)0.45072611
Kurtosis-1.2013772
Mean5.6274541 × 109
Median Absolute Deviation (MAD)2.2071921 × 109
Skewness0.0037792107
Sum1.0907131 × 1014
Variance6.4335302 × 1018
MonotonicityNot monotonic
2026-01-04T17:26:15.078640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81327596881
 
< 0.1%
70176660171
 
< 0.1%
40143811361
 
< 0.1%
98598380911
 
< 0.1%
18668479911
 
< 0.1%
71052310981
 
< 0.1%
22744246441
 
< 0.1%
51337759001
 
< 0.1%
13753043921
 
< 0.1%
91446823291
 
< 0.1%
Other values (19372)19372
99.9%
ValueCountFrequency (%)
12349590181
< 0.1%
12359377671
< 0.1%
12362845481
< 0.1%
12365941471
< 0.1%
12370081331
< 0.1%
12384805081
< 0.1%
12389372601
< 0.1%
12396980401
< 0.1%
12400915971
< 0.1%
12403495301
< 0.1%
ValueCountFrequency (%)
99998730751
< 0.1%
99998349731
< 0.1%
99997154671
< 0.1%
99992984211
< 0.1%
99991600621
< 0.1%
99984532781
< 0.1%
99983754291
< 0.1%
99972687071
< 0.1%
99970609021
< 0.1%
99961544881
< 0.1%

video_duration_sec
Real number (ℝ)

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.421732
Minimum5
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.6 KiB
2026-01-04T17:26:15.178406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q118
median32
Q347
95-th percentile58
Maximum60
Range55
Interquartile range (IQR)29

Descriptive statistics

Standard deviation16.229967
Coefficient of variation (CV)0.50058916
Kurtosis-1.2108439
Mean32.421732
Median Absolute Deviation (MAD)14
Skewness0.0034523581
Sum628398
Variance263.41183
MonotonicityNot monotonic
2026-01-04T17:26:15.295467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20380
 
2.0%
6378
 
2.0%
57376
 
1.9%
8373
 
1.9%
34370
 
1.9%
16370
 
1.9%
26369
 
1.9%
15367
 
1.9%
10365
 
1.9%
52365
 
1.9%
Other values (46)15669
80.8%
ValueCountFrequency (%)
5337
1.7%
6378
2.0%
7359
1.9%
8373
1.9%
9319
1.6%
10365
1.9%
11340
1.8%
12345
1.8%
13352
1.8%
14357
1.8%
ValueCountFrequency (%)
60349
1.8%
59323
1.7%
58355
1.8%
57376
1.9%
56343
1.8%
55332
1.7%
54353
1.8%
53342
1.8%
52365
1.9%
51343
1.8%
Distinct19012
Distinct (%)99.6%
Missing298
Missing (%)1.5%
Memory size2.8 MiB
2026-01-04T17:26:15.665956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length182
Median length153
Mean length89.093534
Min length31

Characters and Unicode

Total characters1700261
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18940 ?
Unique (%)99.2%

Sample

1st rowsomeone shared with me that drone deliveries are already happening and will become common by 2025
2nd rowsomeone shared with me that there are more microorganisms in one teaspoon of soil than people on the planet
3rd rowsomeone shared with me that american industrialist andrew carnegie had a net worth of $475 million usd, worth over $300 billion usd today
4th rowsomeone shared with me that the metro of st. petersburg, with an average depth of hundred meters, is the deepest metro in the world
5th rowsomeone shared with me that the number of businesses allowing employees to bring pets to the workplace has grown by 6% worldwide
ValueCountFrequency (%)
that19188
 
6.3%
the19074
 
6.2%
a15219
 
5.0%
is10469
 
3.4%
in7863
 
2.6%
my7299
 
2.4%
of6670
 
2.2%
on5551
 
1.8%
to4169
 
1.4%
are3980
 
1.3%
Other values (1316)207028
67.5%
2026-01-04T17:26:16.169463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
293662
17.3%
e172706
 
10.2%
a133223
 
7.8%
t127604
 
7.5%
i103191
 
6.1%
n99064
 
5.8%
s94411
 
5.6%
o91798
 
5.4%
r83088
 
4.9%
l67749
 
4.0%
Other values (42)433765
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1700261
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
293662
17.3%
e172706
 
10.2%
a133223
 
7.8%
t127604
 
7.5%
i103191
 
6.1%
n99064
 
5.8%
s94411
 
5.6%
o91798
 
5.4%
r83088
 
4.9%
l67749
 
4.0%
Other values (42)433765
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1700261
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
293662
17.3%
e172706
 
10.2%
a133223
 
7.8%
t127604
 
7.5%
i103191
 
6.1%
n99064
 
5.8%
s94411
 
5.6%
o91798
 
5.4%
r83088
 
4.9%
l67749
 
4.0%
Other values (42)433765
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1700261
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
293662
17.3%
e172706
 
10.2%
a133223
 
7.8%
t127604
 
7.5%
i103191
 
6.1%
n99064
 
5.8%
s94411
 
5.6%
o91798
 
5.4%
r83088
 
4.9%
l67749
 
4.0%
Other values (42)433765
25.5%

verified_status
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
not verified
18142 
verified
 
1240

Length

Max length12
Median length12
Mean length11.744092
Min length8

Characters and Unicode

Total characters227624
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot verified
2nd rownot verified
3rd rownot verified
4th rownot verified
5th rownot verified

Common Values

ValueCountFrequency (%)
not verified18142
93.6%
verified1240
 
6.4%

Length

2026-01-04T17:26:16.265013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-04T17:26:16.336526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
verified19382
51.7%
not18142
48.3%

Most occurring characters

ValueCountFrequency (%)
i38764
17.0%
e38764
17.0%
f19382
8.5%
d19382
8.5%
r19382
8.5%
v19382
8.5%
o18142
8.0%
n18142
8.0%
t18142
8.0%
18142
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)227624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i38764
17.0%
e38764
17.0%
f19382
8.5%
d19382
8.5%
r19382
8.5%
v19382
8.5%
o18142
8.0%
n18142
8.0%
t18142
8.0%
18142
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)227624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i38764
17.0%
e38764
17.0%
f19382
8.5%
d19382
8.5%
r19382
8.5%
v19382
8.5%
o18142
8.0%
n18142
8.0%
t18142
8.0%
18142
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)227624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i38764
17.0%
e38764
17.0%
f19382
8.5%
d19382
8.5%
r19382
8.5%
v19382
8.5%
o18142
8.0%
n18142
8.0%
t18142
8.0%
18142
8.0%

author_ban_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
active
15663 
under review
2080 
banned
1639 

Length

Max length12
Median length6
Mean length6.6438964
Min length6

Characters and Unicode

Total characters128772
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunder review
2nd rowactive
3rd rowactive
4th rowactive
5th rowactive

Common Values

ValueCountFrequency (%)
active15663
80.8%
under review2080
 
10.7%
banned1639
 
8.5%

Length

2026-01-04T17:26:16.407370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-04T17:26:16.470111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
active15663
73.0%
under2080
 
9.7%
review2080
 
9.7%
banned1639
 
7.6%

Most occurring characters

ValueCountFrequency (%)
e23542
18.3%
i17743
13.8%
v17743
13.8%
a17302
13.4%
t15663
12.2%
c15663
12.2%
n5358
 
4.2%
r4160
 
3.2%
d3719
 
2.9%
u2080
 
1.6%
Other values (3)5799
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)128772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e23542
18.3%
i17743
13.8%
v17743
13.8%
a17302
13.4%
t15663
12.2%
c15663
12.2%
n5358
 
4.2%
r4160
 
3.2%
d3719
 
2.9%
u2080
 
1.6%
Other values (3)5799
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)128772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e23542
18.3%
i17743
13.8%
v17743
13.8%
a17302
13.4%
t15663
12.2%
c15663
12.2%
n5358
 
4.2%
r4160
 
3.2%
d3719
 
2.9%
u2080
 
1.6%
Other values (3)5799
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)128772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e23542
18.3%
i17743
13.8%
v17743
13.8%
a17302
13.4%
t15663
12.2%
c15663
12.2%
n5358
 
4.2%
r4160
 
3.2%
d3719
 
2.9%
u2080
 
1.6%
Other values (3)5799
 
4.5%

video_view_count
Real number (ℝ)

High correlation  Missing 

Distinct15632
Distinct (%)81.9%
Missing298
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean254708.56
Minimum20
Maximum999817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.6 KiB
2026-01-04T17:26:16.562130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile936
Q14942.5
median9954.5
Q3504327
95-th percentile903662.8
Maximum999817
Range999797
Interquartile range (IQR)499384.5

Descriptive statistics

Standard deviation322893.28
Coefficient of variation (CV)1.267697
Kurtosis-0.63510685
Mean254708.56
Median Absolute Deviation (MAD)9829.5
Skewness0.92846093
Sum4.8608581 × 109
Variance1.0426007 × 1011
MonotonicityNot monotonic
2026-01-04T17:26:16.678800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73937
 
< 0.1%
25627
 
< 0.1%
31846
 
< 0.1%
40816
 
< 0.1%
20306
 
< 0.1%
85535
 
< 0.1%
89975
 
< 0.1%
70905
 
< 0.1%
29865
 
< 0.1%
8315
 
< 0.1%
Other values (15622)19027
98.2%
(Missing)298
 
1.5%
ValueCountFrequency (%)
202
< 0.1%
221
 
< 0.1%
231
 
< 0.1%
241
 
< 0.1%
271
 
< 0.1%
281
 
< 0.1%
291
 
< 0.1%
312
< 0.1%
351
 
< 0.1%
373
< 0.1%
ValueCountFrequency (%)
9998171
< 0.1%
9996731
< 0.1%
9996551
< 0.1%
9996531
< 0.1%
9994461
< 0.1%
9993461
< 0.1%
9991321
< 0.1%
9991271
< 0.1%
9990821
< 0.1%
9989111
< 0.1%

video_like_count
Real number (ℝ)

High correlation  Missing 

Distinct12224
Distinct (%)64.1%
Missing298
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean84304.636
Minimum0
Maximum657830
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size151.6 KiB
2026-01-04T17:26:16.794763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile83.15
Q1810.75
median3403.5
Q3125020
95-th percentile393957.3
Maximum657830
Range657830
Interquartile range (IQR)124209.25

Descriptive statistics

Standard deviation133420.55
Coefficient of variation (CV)1.5826004
Kurtosis2.4901651
Mean84304.636
Median Absolute Deviation (MAD)3365.5
Skewness1.7868774
Sum1.6088697 × 109
Variance1.7801042 × 1010
MonotonicityNot monotonic
2026-01-04T17:26:16.903137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3222
 
0.1%
1919
 
0.1%
1018
 
0.1%
618
 
0.1%
918
 
0.1%
417
 
0.1%
6017
 
0.1%
517
 
0.1%
1217
 
0.1%
216
 
0.1%
Other values (12214)18905
97.5%
(Missing)298
 
1.5%
ValueCountFrequency (%)
04
 
< 0.1%
116
0.1%
216
0.1%
316
0.1%
417
0.1%
517
0.1%
618
0.1%
713
0.1%
811
0.1%
918
0.1%
ValueCountFrequency (%)
6578301
< 0.1%
6562431
< 0.1%
6545881
< 0.1%
6535611
< 0.1%
6496951
< 0.1%
6481011
< 0.1%
6472361
< 0.1%
6398771
< 0.1%
6368121
< 0.1%
6353351
< 0.1%

video_share_count
Real number (ℝ)

High correlation  Missing 

Distinct9231
Distinct (%)48.4%
Missing298
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean16735.248
Minimum0
Maximum256130
Zeros99
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size151.6 KiB
2026-01-04T17:26:17.012539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q1115
median717
Q318222
95-th percentile89017.5
Maximum256130
Range256130
Interquartile range (IQR)18107

Descriptive statistics

Standard deviation32036.174
Coefficient of variation (CV)1.9142933
Kurtosis8.3386485
Mean16735.248
Median Absolute Deviation (MAD)709
Skewness2.7226563
Sum3.1937548 × 108
Variance1.0263165 × 109
MonotonicityNot monotonic
2026-01-04T17:26:17.125311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2147
 
0.8%
1144
 
0.7%
3122
 
0.6%
5109
 
0.6%
099
 
0.5%
895
 
0.5%
495
 
0.5%
690
 
0.5%
1276
 
0.4%
976
 
0.4%
Other values (9221)18031
93.0%
(Missing)298
 
1.5%
ValueCountFrequency (%)
099
0.5%
1144
0.7%
2147
0.8%
3122
0.6%
495
0.5%
5109
0.6%
690
0.5%
772
0.4%
895
0.5%
976
0.4%
ValueCountFrequency (%)
2561301
< 0.1%
2496721
< 0.1%
2410101
< 0.1%
2401541
< 0.1%
2380041
< 0.1%
2346181
< 0.1%
2228481
< 0.1%
2207451
< 0.1%
2199171
< 0.1%
2172651
< 0.1%

video_download_count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct4336
Distinct (%)22.7%
Missing298
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1049.4296
Minimum0
Maximum14994
Zeros977
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size151.6 KiB
2026-01-04T17:26:17.230173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median46
Q31156.25
95-th percentile5547.85
Maximum14994
Range14994
Interquartile range (IQR)1149.25

Descriptive statistics

Standard deviation2004.2999
Coefficient of variation (CV)1.9098945
Kurtosis8.4357775
Mean1049.4296
Median Absolute Deviation (MAD)46
Skewness2.7361623
Sum20027315
Variance4017218.1
MonotonicityNot monotonic
2026-01-04T17:26:17.348389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0977
 
5.0%
1972
 
5.0%
2704
 
3.6%
3634
 
3.3%
4556
 
2.9%
5402
 
2.1%
6381
 
2.0%
7315
 
1.6%
8303
 
1.6%
9283
 
1.5%
Other values (4326)13557
69.9%
(Missing)298
 
1.5%
ValueCountFrequency (%)
0977
5.0%
1972
5.0%
2704
3.6%
3634
3.3%
4556
2.9%
5402
2.1%
6381
 
2.0%
7315
 
1.6%
8303
 
1.6%
9283
 
1.5%
ValueCountFrequency (%)
149941
< 0.1%
144171
< 0.1%
143081
< 0.1%
141461
< 0.1%
140441
< 0.1%
139541
< 0.1%
138591
< 0.1%
137711
< 0.1%
136532
< 0.1%
135521
< 0.1%

video_comment_count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct2424
Distinct (%)12.7%
Missing298
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean349.31215
Minimum0
Maximum9599
Zeros3434
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size151.6 KiB
2026-01-04T17:26:17.462076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q3292
95-th percentile1921
Maximum9599
Range9599
Interquartile range (IQR)291

Descriptive statistics

Standard deviation799.63886
Coefficient of variation (CV)2.2891814
Kurtosis19.711106
Mean349.31215
Median Absolute Deviation (MAD)9
Skewness3.8946817
Sum6666273
Variance639422.31
MonotonicityNot monotonic
2026-01-04T17:26:17.578087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03434
 
17.7%
12222
 
11.5%
21097
 
5.7%
3788
 
4.1%
4545
 
2.8%
5432
 
2.2%
6320
 
1.7%
7272
 
1.4%
8236
 
1.2%
9203
 
1.0%
Other values (2414)9535
49.2%
(Missing)298
 
1.5%
ValueCountFrequency (%)
03434
17.7%
12222
11.5%
21097
 
5.7%
3788
 
4.1%
4545
 
2.8%
5432
 
2.2%
6320
 
1.7%
7272
 
1.4%
8236
 
1.2%
9203
 
1.0%
ValueCountFrequency (%)
95991
< 0.1%
86741
< 0.1%
84811
< 0.1%
84701
< 0.1%
78191
< 0.1%
77671
< 0.1%
76941
< 0.1%
76051
< 0.1%
74581
< 0.1%
74111
< 0.1%

Interactions

2026-01-04T17:26:13.401709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:08.988682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.648828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.438282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.279739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.954779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.720752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.669641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.087430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.737142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.536849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.367266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.050573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.816805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.759204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.175378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.822461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.677535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.460722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.211255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.918935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.846466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.260567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.916317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.793693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.566963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.318915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.013467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.934733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.351483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.010300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.945441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.660766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.428997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.112518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:14.025540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.442222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.111428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.058355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.755722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.528853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.207018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:14.119638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:09.563483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:10.229804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.179120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:11.858346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:12.628883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-04T17:26:13.308212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-04T17:26:17.691284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
author_ban_statusclaim_statusverified_statusvideo_comment_countvideo_download_countvideo_duration_secvideo_idvideo_like_countvideo_share_countvideo_view_count
author_ban_status1.0000.3160.0600.0800.1210.0120.0000.1620.1200.204
claim_status0.3161.0000.1700.3590.5240.0150.0000.7040.5070.900
verified_status0.0600.1701.0000.0590.0910.0000.0070.1180.0770.152
video_comment_count0.0800.3590.0591.0000.951-0.0060.0060.9000.8570.835
video_download_count0.1210.5240.0910.9511.0000.0060.0060.9390.8900.862
video_duration_sec0.0120.0150.000-0.0060.0061.0000.0090.0050.0050.003
video_id0.0000.0000.0070.0060.0060.0091.0000.0050.0010.004
video_like_count0.1620.7040.1180.9000.9390.0050.0051.0000.9400.910
video_share_count0.1200.5070.0770.8570.8900.0050.0010.9401.0000.864
video_view_count0.2040.9000.1520.8350.8620.0030.0040.9100.8641.000

Missing values

2026-01-04T17:26:14.254490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-04T17:26:14.379946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-04T17:26:14.592000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

claim_statusvideo_idvideo_duration_secvideo_transcription_textverified_statusauthor_ban_statusvideo_view_countvideo_like_countvideo_share_countvideo_download_countvideo_comment_count
0claim701766601759someone shared with me that drone deliveries are already happening and will become common by 2025not verifiedunder review343296.019425.0241.01.00.0
1claim401438113632someone shared with me that there are more microorganisms in one teaspoon of soil than people on the planetnot verifiedactive140877.077355.019034.01161.0684.0
2claim985983809131someone shared with me that american industrialist andrew carnegie had a net worth of $475 million usd, worth over $300 billion usd todaynot verifiedactive902185.097690.02858.0833.0329.0
3claim186684799125someone shared with me that the metro of st. petersburg, with an average depth of hundred meters, is the deepest metro in the worldnot verifiedactive437506.0239954.034812.01234.0584.0
4claim710523109819someone shared with me that the number of businesses allowing employees to bring pets to the workplace has grown by 6% worldwidenot verifiedactive56167.034987.04110.0547.0152.0
5claim897220095535someone shared with me that gross domestic product (gdp) is the best financial indicator of a country's overall trade potentialnot verifiedunder review336647.0175546.062303.04293.01857.0
6claim495888699216someone shared with me that elvis presley has sold more records than the music band the beatlesnot verifiedactive750345.0486192.0193911.08616.05446.0
7claim227098226341someone shared with me that the best selling single of all time is "white christmas" by bing crosbynot verifiedactive547532.01072.050.022.011.0
8claim523576969250someone shared with me that about half of the world's population can access the web via a mobile devicenot verifiedactive24819.010160.01050.053.027.0
9claim466086109445someone shared with me that it would take a 50 petabyte drive to store every written work ever createdverifiedactive931587.0171051.067739.04104.02540.0
claim_statusvideo_idvideo_duration_secvideo_transcription_textverified_statusauthor_ban_statusvideo_view_countvideo_like_countvideo_share_countvideo_download_countvideo_comment_count
19372NaN573176652716NaNnot verifiedactiveNaNNaNNaNNaNNaN
19373NaN573183807246NaNverifiedactiveNaNNaNNaNNaNNaN
19374NaN355982512742NaNnot verifiedactiveNaNNaNNaNNaNNaN
19375NaN215979736745NaNverifiedactiveNaNNaNNaNNaNNaN
19376NaN40995385657NaNnot verifiedactiveNaNNaNNaNNaNNaN
19377NaN757822684021NaNnot verifiedactiveNaNNaNNaNNaNNaN
19378NaN607923617953NaNnot verifiedactiveNaNNaNNaNNaNNaN
19379NaN256553968510NaNverifiedunder reviewNaNNaNNaNNaNNaN
19380NaN296917854024NaNnot verifiedactiveNaNNaNNaNNaNNaN
19381NaN813275968813NaNnot verifiedactiveNaNNaNNaNNaNNaN